This document presents a mathematical model for minimizing power losses on electric transmission lines. The model considers ohmic and corona losses. Ohmic loss is from resistance of conductors, while corona loss occurs due to ionization of air around high-voltage conductors. The model forms a nonlinear optimization problem that is minimized using classical techniques. The results show that power losses are minimized when transmission uses very low current, an operating voltage close to the critical disruptive voltage, and large spacing between conductors relative to their diameters. These findings match existing principles of efficient power transmission.

1. Mathematical Theory and Modeling www.iiste.org
ISSN 2224-5804 (Paper) ISSN 2225-0522 (Online)
Vol.3, No.7, 2013
28
Minimization of Losses on Electric Power Transmission Lines
1
M.O. OKE and 2
O.M. BAMIGBOLA
1
Department of Electrical & Electronic Engineering, Ekiti State University
Ado – Ekiti, Nigeria
E- mail: femioke91@gmail.com
2
Department of Mathematics, University of Ilorin, Ilorin, Nigeria
E-mail: ombamigbola@hotmail.com
Abstract
Availability of electric power has been the most powerful vehicle for facilitating economic, industrial and
social developments of any nation. Electric power is transmitted by means of transmission lines which deliver
bulk power from generating stations to load centres and consumers. For electric power to get to the final
consumers in proper form and quality, losses along the lines must be reduced to the barest minimum. In this
paper, a mathematical model of losses along electric power transmission lines was developed using a
combination of ohmic and corona losses. The resulting model, which is a nonlinear multivariable unconstrained
optimization problem, was minimized using the classical optimization technique. From the results, we were able
to see that power losses on transmission lines will be minimized if we transmit electric power at a very low
current and at an operating voltage that is very close to the critical disruptive voltage. Also the spacing between
the conductors should be large in comparison to their diameters. These results, gotten by the use of an analytical
method, conform to the existing results for power transmission.
Keywords: Power Losses, Minimization, Transmission, Classical Optimization, Mathematical Model.
1. Introduction
Electrical energy is generated at power stations which are usually located far away from load centres. Thus, a
network of conductors between the power stations and the consumers is required in order to harness the power
generated. This network of conductors may be divided into two main components, namely, the transmission
system and the distribution system. Accurate knowledge of power losses on transmission lines and their
minimization is a critical component for efficient flow of power in an electrical network. Power losses result in
lower power availability to final consumers. Hence, adequate measures need to be taken to reduce power losses
to the barest minimum.
Power plants' planning in a way to meet the power network load demand is one of the most important and
essential issues in power systems. Since transmission lines connect generating plants and substations in power
network, the analysis, computation and reduction of transmission losses in these networks are of great concern to
scientists and engineers.
A lot of research works have been carried out on the analysis, computation and reduction of transmission
losses. Zakariya (2010) made a comparison between the corona power loss associated with HVDC transmission
lines and the ohmic power loss. The corona power loss and ohmic power loss were measured and computed for
different transmission line configurations and under fair weather and rainy conditions. Numphetch et al. (2011)
worked on loss minimization using optimal power flow based on swarm intelligences. Thabendra et al. (2009)
considered multi-objective optimization methods for power loss minimization and voltage stability while
Abdullah et al. (2010) looked at transmission loss minimization and power installation cost using evolutionary
computation for improvement of voltage stability. Bagriyanik et al. (2003) used a fuzzy multi-objective
optimization and genetic algorithm-based method to find optimum power system operating conditions. In
addition to active power losses, series reactive power losses of transmission system were also considered as one
of the multiple objectives. Onohaebi and Odiase (2010) considered the relationship between distance and
loadings on power losses using the existing 330 KV Nigerian transmission network as a case study in his
empirical modelling of power losses as a function of line loadings and lengths while Moghadam and
Berahmandpour (2010) developed a new method for calculating transmission power losses based on exact
modelling of ohmic loss, to mention a few. In all these research work, much emphasis has been on reduction of
losses using design and construction technique, optimal power flow based on swarm intelligences and
evolutionary methods. A better approach would have been the one that utilizes the concept of classical
optimization for minimizing the losses by employing mathematical modelling and differential calculus as
working tools.
Therefore, in this paper, we formulated a mathematical model for power losses, on transmission lines, by
making use of the formulae for ohmic and corona losses. The resulting model was minimized using the classical
optimization technique and the results are in agreement with the existing rules of power transmission.

2. Mathematical Theory and Modeling www.iiste.org
ISSN 2224-5804 (Paper) ISSN 2225-0522 (Online)
Vol.3, No.7, 2013
29
2. Mathematical Model for Power Losses
The main reason for losses in transmission and sub-transmission lines is the resistance of conductors against the
flow of current. The production of heat in the conductor as a result of the flow of current increases its
temperature. This rise in the conductor's temperature further increases the resistance of the conductor and this
will consequently increase the losses. This implies that ohmic power loss is the main component of losses in
transmission and sub-transmission lines, Mehta and Mehta (2008) and Gupta (2008).
The value of the ohmic power loss, Wadhwa (2009), is given as
= / / ℎ (1)
where
I denotes current along the conductor and
R represents resistance of the conductor.
The formation of corona on transmission line is associated with a loss of power, which will have some effect on
the efficiency of the transmission line. The corona power loss for a fair weather condition, Mehta and Mehta
(2008), Wadhwa (2009), Gupta (2008) and James (2005), has the value
= 242
( !)
"
. $(%
). (& − &() . 10*!
/ / ℎ (2)
where
+ represents the frequency of transmission,
, denotes the air density factor,
- is radius of the conductor,
. represents the space between the transmission lines,
& is the operating voltage and
&(denotes the distruptive voltage.
Taking the total power loss on transmission lines to be the summation of ohmic and corona loss, we have
/0 11 = + (3)
i.e
/0 11 = + 242
( !)
"
. $(%
). (& − &() . 10*!
/ / ℎ (4)
The general form of equation (4) is given by
/0 11 =
40
5
+ 242
( !)
"
$(
5
6%7)
8
. (& − &() . 10*!
/ / ℎ (5)
where
: is the resistivity of the conductor,
denotes the length of the conductor and
; is the cross-sectional area of the conductor.
3. Minimization of Power Losses
The problem of finding the optimum electric power loss during transmission can therefore be posed as
<=>= =?
( , &, .)
/0 11 =
40
5
+ 242
( !)
"
$(
5
6%7)
8
. (& − &() . 10*!
/ / ℎ (6)
This is a nonlinear multivariable unconstrained optimization problem. Assuming that the transmission related
factors are continuous, then (6) can be solved using the classical method of optimization.
To determine the stationary points of (6), Rao (1998), we differentiate with respect to the selected variables to
get
BCDEFF
BG
=
G40
5
(7)
BCDEFF
BI
= 484
( !)
"
. $(
5
6%7)
8
. (& − &()10*!
(8)
BCDEFF
B%
= −121
( !)
"
. $(
5
6
)
8
. (& − &() .*
K
710*!
(9)
Equations (7), (8) and (9) give the extremum points as = 0, & = &( >. . → ∞.
The second derivatives with respect to the variables are
B7CDEFF
BG7 =
40
5
(10)
B7CDEFF
BGBI
= 0 (11)
B7CDEFF
BGB%
= 0 (12)

3. Mathematical Theory and Modeling www.iiste.org
ISSN 2224-5804 (Paper) ISSN 2225-0522 (Online)
Vol.3, No.7, 2013
30
B7CDEFF
BIBG
= 0 (13)
B7CDEFF
BI7 = 484
( !)
"
. $(
5
6%7)
8
. 10*!
(14)
B7CDEFF
BIB%
= −242
( !)
"
. $O
5
6
P
8
. (& − &().*
K
710*!
(15)
B7CDEFF
B%BG
= 0 (16)
B7CDEFF
B%BI
= −242
( !)
"
. $O
5
6
P
8
. (& − &().*
K
710*!
(17)
B7CDEFF
B%7 =
QRQ
.
( !)
"
. $(
5
6
)
8
. (& − &() .*
S
710*!
(18)
The Hessian matrix, Rao (1998), is therefore given by
T =
U
V
W
B7CDEFF
BG7
B7CDEFF
BGBI
B7CDEFF
BGB%
B7CDEFF
BIBG
B7CDEFF
BI7
B7CDEFF
BIB%
B7CDEFF
B%BG
B7CDEFF
B%BI
B7CDEFF
B%7 X
Y
Z
i.e.,
T =
U
V
V
V
V
V
W
2:
;
0 0
0 484
(+ + 25)
,
. [(
;
.
)
8
. 10*!
−242
(+ + 25)
,
. []
;
^
8
. (& − &().*
Q
10*!
0 −242
(+ + 25)
,
. []
;
^
8
. (& − &().*
Q
10*!
363
2
.
(+ + 25)
,
. [(
;
)
8
. (& − &() .*
!
10*!
X
Y
Y
Y
Y
Y
Z
for which,
T_ = `
40
5
` > 0
T = b
40
5
0
0 484
( !)
"
. $(
5
6%7)
8
. 10*!
b =
40
5
. 484
( !)
"
. $(
5
6%7)
8
. 10*!
> 0
TQ =
b
b
b
2:
;
0 0
0 484
(+ + 25)
,
. [(
;
.
)
8
. 10*!
−242
(+ + 25)
,
. []
;
^
8
. (& − &().*
Q
10*!
0 −242
(+ + 25)
,
. []
;
^
8
. (& − &().*
Q
10*!
363
2
.
(+ + 25)
,
. [(
;
)
8
. (& − &() .*
!
10*!
b
b
b
=
2:
;
cd484
(+ + 25)
,
. []
;
.
^
8
. 10*!
e d
363
2
.
(+ + 25)
,
. []
;
^
8
. (& − &() .*
!
10*!
e
− (242
(+ + 25)
,
. []
;
^
8
. (& − &().*
Q
10*!
) f > 0
4. Discussion on Results
Since T_, T and TQ are all greater than zero, then it shows that the Hessian matrix of power losses over
transmission lines is positive definite at the extremum values. Hence the power loss is minimum at = 0, & =
&( >. . → ∞.
Technically, this implies that the total power losses on transmission lines will only be minimum if

4. Mathematical Theory and Modeling www.iiste.org
ISSN 2224-5804 (Paper) ISSN 2225-0522 (Online)
Vol.3, No.7, 2013
31
(i) power is transmitted at a very low current along transmission lines. This will reduce the ohmic or
line loss on the conductors to the barest minimum. This conforms to the principle of electric power
transmission.
(ii) the operating voltage is equal to the critical disruptive voltage. When this happens, there is no
ionisation of air around the conductor and hence no corona is formed. Therefore, there will be no
corona loss and
(iii) the spacing between the conductors on the transmission line should be large. This is because; an
increase in the spacing between conductors reduces the electro-static stresses. This therefore
reduces the corona effect. If the spacing between the conductors is made very large as compared to
their diameter, there may not be any corona effect or losses on the line.
Conclusion
The classical optimization technique has been used to minimize power losses on transmission lines thereby
getting the same results for optimum power transmission by making use of an analytical method. The application
of the classical optimization technique to the mathematical model of power losses on transmission lines has
therefore provide a better understanding of the problem of power losses on high voltage transmission lines.
References
Zakariya, M.A. (2010), “Corona Power Loss versus Ohmic Power Loss in HV Transmission Lines”, Proceedings
of the Dhahran Power Conference.
Numphetch, S., Uthen, L., Umaporn, K., Dusit, U. & Thanatchai, K. (2011), “Loss Minimization Using Optimal
Power Flow based on Swarm Intelligences”, ECTI Transactions on Electrical, Electronic and Communication
Engineering, 9(1), 212-222.
Thabendra, T., Yaw, N., Sanjeev, K.S., Bhuvana, R, & David, A.C. (2009), “Multi-Objective Optimization
Methods for Power Loss Minimization and Voltage Stability”, Journal of Advanced Power Systems, 9(2), 1-10.
Abddullah N.R.H., Ismail M. & Mohammad M.O. (2010), “Transmission Loss Minimization and Power
Installation Cost using Evolutionary Computation for Improvement of Voltage Stability”, Proceedings of the
14th International Middle East Power Systems Conference.
Bagriyanik F. G., Aygen Z. E. & Bagriyanik M. (2003), “Power Loss Minimization Using Fuzzy Multi-objective
Formulation and Genetic Algorithm”, Proceedings of the Bologna IEEE Power Tech Conference.
Onohaebi O.S. & Odiase O.F. (2010), “Empirical Modelling of Power Losses as a Function of Line Loadings
and Lengths in the Nigeria 330KV Transmission Lines”, International Journal of Academic Research, 2(3), 47 -
53.
Moghadam M. F. & Berahmandpour H. A. (2010), “A New Method for Calculating Transmission Power Losses
Based on Exact Modeling of Ohmic Loss”, Proceedings of the 25th International Power Conference.
Mehta V. K. & Mehta R. (2008), “Principles of Power Systems”, S. Chand Company Ltd.
Gupta J.B. (2008), “A Course in Power Systems”, S.K. Kataria and Sons, Publisher of Engineering and
Computer Books.
Wadhwa C.L. (2009), “Electrical Power Systems”, New Age International (P) Publishers.
James A. M. (2005), “Electric Power System Applications of Optimization”, McGraw-Hill Company Ltd.
Rao S.S. (1998), “Optimization Theory and Application”, Wiley Eastern Limited.

5. This academic article was published by The International Institute for Science,
Technology and Education (IISTE). The IISTE is a pioneer in the Open Access
Publishing service based in the U.S. and Europe. The aim of the institute is
Accelerating Global Knowledge Sharing.
More information about the publisher can be found in the IISTE’s homepage:
http://www.iiste.org
CALL FOR PAPERS
The IISTE is currently hosting more than 30 peer-reviewed academic journals and
collaborating with academic institutions around the world. There’s no deadline for
submission. Prospective authors of IISTE journals can find the submission
instruction on the following page: http://www.iiste.org/Journals/
The IISTE editorial team promises to the review and publish all the qualified
submissions in a fast manner. All the journals articles are available online to the
readers all over the world without financial, legal, or technical barriers other than
those inseparable from gaining access to the internet itself. Printed version of the
journals is also available upon request of readers and authors.
IISTE Knowledge Sharing Partners
EBSCO, Index Copernicus, Ulrich's Periodicals Directory, JournalTOCS, PKP Open
Archives Harvester, Bielefeld Academic Search Engine, Elektronische
Zeitschriftenbibliothek EZB, Open J-Gate, OCLC WorldCat, Universe Digtial
Library , NewJour, Google Scholar